Navigation Using Crowdsourcing Data

ABSTRACT

Method, computer program product, and apparatus for providing navigation guidance to vehicles are disclosed. The method may include receiving crowdsourcing data from a plurality of vehicles, determining information corresponding to a route of interest to at least one vehicle using the crowdsourcing data, and providing navigation guidance to the at least one vehicle using the information determined. The crowdsourcing data includes on board diagnostics data (OBD) correlated with time stamps and GPS locations of a vehicle, where the on board diagnostics data includes odometer information, speedometer information, fuel consumption information, steering information, and impact data.

FIELD

The present disclosure relates to the field of wireless communications.In particular, the present disclosure relates to providing navigationguidance to vehicles.

BACKGROUND

Conventional personal navigation devices (PNDs) make decisionsconcerning routes based on simple metrics, such as 1) shortest routefrom a start location to a destination location, which may possibly bealtered to exclude toll roads or freeways; or 2) shortest time based onrecorded speed limits for the possible routes available. Some PNDs canconnect online in order to get congestion data, which relies on sensorboxes and even manual counts by people on the scene. Thus, suchcongestion data is patchy and may only apply to well-traveled roadsegments. In addition, such conventional data gathering methods can beuneconomical for most roads. Furthermore, conventional PNDs may notprovide assistance to users for determining parking conditions at thedestination, and conventional PNDs may not take into account the currentvehicle conditions before planning a route.

Therefore, there is a need for method, computer program product, andapparatus that can address the above issues of the conventional methodsand devices.

SUMMARY

The present disclosure relates to providing navigation guidance tovehicles. According to embodiments of the present disclosure, a methodmay include receiving crowdsourcing data from a plurality of vehicles,determining information corresponding to a route of interest to at leastone vehicle using the crowdsourcing data, and providing navigationguidance to the at least one vehicle using the information determined.The crowdsourcing data includes on board diagnostics data (OBD)correlated with time stamps and GPS locations of a vehicle, where the onboard diagnostics data includes odometer information, speedometerinformation, fuel consumption information, steering information, andimpact data.

According to aspects of the present disclosure, The method ofdetermining information corresponding to a route of interest to at leastone vehicle comprises at least one of: predicting closing of a laneusing the crowdsourcing data; predicting an avoidance maneuver using thecrowdsourcing data; predicting a congestion with respect to a segment ofthe route of the at least one vehicle using the crowdsourcing data; andpredicting traffic light patterns using the crowdsourcing data. Themethod of predicting closing of a lane comprises monitoring changes ofvehicle direction at a location on the route of the at least onevehicle, and determining a lane is closed in response to a number ofchanges of vehicle direction being larger than a predetermined thresholdvalue. The method of predicting an avoidance maneuver comprisesmonitoring change of vehicle direction and distance traveled at a closevicinity of a location on the route of the at least one vehicle, anddetermining an avoidance maneuver in response to a ratio of change ofvehicle direction and distance traveled being less than a predeterminedthreshold value.

The method of determining information corresponding to a route ofinterest to at least one vehicle further comprises at least one of:determining a route based at least in part on an amount of timepredicted for travelling from a starting location to a destinationlocation of the route using the crowdsourcing data; and determining aroute based at least in part on a predicted fuel consumption of theroute using the crowdsourcing data.

The method of determining information corresponding to a route ofinterest to at least one vehicle further comprises at least one of:monitoring a distance traveled by the at least one vehicle afterreaching a destination, and predicting availability of parking spaces atthe destination based at least in part on the distance traveled; andmonitoring an amount of time traveled by the at least one vehicle afterreaching a destination, and predicting availability of parking spaces atthe destination based at least in part on the amount of time traveled.

The method of determining information corresponding to a route ofinterest to at least one vehicle further comprises: measuring a timetaken to travel a predefined percent of the route until the at least onevehicle comes to a halt at a predetermined location; and predicting anaverage amount of time used to find parking at the predeterminedlocation using the time taken to travel a predefined percent of theroute.

The method of determining information corresponding to a route ofinterest to at least one vehicle further comprises at least one of:determining popularity of a fueling station along the route; determiningtype of fuel sold at the fueling station along the route; determiningpopularity of a business along the route; and determining popularity ofa rest area along the route. The method of determining informationcorresponding to a route of interest to at least one vehicle furthercomprises determining location of a stolen vehicle in accordance withcrowdsourcing data received from the stolen vehicle.

In yet another embodiment, a non-transitory medium storing instructionsfor execution by one or more computer systems, the instructions compriseinstructions for receiving crowdsourcing data from a plurality ofvehicles, instructions for determining information corresponding to aroute of interest to at least one vehicle using the crowdsourcing data,and instructions for providing navigation guidance to the at least onevehicle using the information determined.

In yet another embodiment, an apparatus comprises a control unitincluding processing logic. The processing logic comprises logicconfigured to receive crowdsourcing data from a plurality of vehicles,logic configured to determine information corresponding to a route ofinterest to at least one vehicle using the crowdsourcing data, and logicconfigured to provide navigation guidance to the at least one vehicleusing the information determined.

In yet another embodiment, a mobile station comprises a navigationcontroller including processing logic. The processing logic compriseslogic configured to send crowdsourcing data to a server, logicconfigured to receive navigation guidance from the server, and logicconfigured to display the navigation guidance on a display. The logicconfigured to send crowdsourcing data to a server further compriseslogic configured to receive on board diagnostics data from an on-boarddiagnostic module of a vehicle, logic configured to receive GPSlocations of the vehicle from a GNSS module, and logic configured tocorrelate the on board diagnostics data and GPS locations of the vehiclewith time stamps to generate the crowdsourcing data. The on boarddiagnostics data comprises odometer information, speedometerinformation, fuel consumption information, steering information, andimpact data.

In yet another embodiment, a system comprises means for receivingcrowdsourcing data from a plurality of vehicles, means for determininginformation corresponding to a route of interest to at least one vehicleusing the crowdsourcing data, and means for providing navigationguidance to the at least one vehicle using the information determined.

BRIEF DESCRIPTION OF THE DRAWINGS

The aforementioned features and advantages of the disclosure, as well asadditional features and advantages thereof, will be more clearlyunderstandable after reading detailed descriptions of embodiments of thedisclosure in conjunction with the following drawings.

FIG. 1 illustrates an exemplary crowdsourcing system according to someaspects of the present disclosure.

FIG. 2 illustrates an exemplary implementation of a crowdsourcing clientaccording to some aspects of the present disclosure.

FIG. 3A illustrates an exemplary method of providing navigation guidanceusing crowdsourcing data according to some aspects of the presentdisclosure.

FIG. 3B illustrates an example of crowdsourcing data according to someaspects of the present disclosure.

FIG. 4 illustrates an exemplary block diagram of a crowdsourcing serveraccording to some aspects of the present disclosure.

FIG. 5 illustrates an exemplary block diagram of a crowdsourcing datacontroller of FIG. 4 according to some aspects of the presentdisclosure.

FIG. 6 illustrates a method of providing navigation guidance accordingto some aspects of the present disclosure.

FIG. 7 illustrates an exemplary application of using crowdsourcing dataaccording to some aspects of the present disclosure.

FIG. 8 illustrates another exemplary application of using crowdsourcingdata according to some aspects of the present disclosure.

Like numbers are used throughout the figures.

DESCRIPTION OF EMBODIMENTS

Embodiments of method, apparatus and computer program product forproviding navigation guidance to vehicles are disclosed. The followingdescriptions are presented to enable any person skilled in the art tomake and use the disclosure. Descriptions of specific embodiments andapplications are provided only as examples. Various modifications andcombinations of the examples described herein will be readily apparentto those skilled in the art, and the general principles defined hereinmay be applied to other examples and applications without departing fromthe spirit and scope of the disclosure. Thus, the present disclosure isnot intended to be limited to the examples described and shown, but isto be accorded the widest scope consistent with the principles andfeatures disclosed herein. The word “exemplary” or “example” is usedherein to mean “serving as an example, instance, or illustration.” Anyaspect or embodiment described herein as “exemplary” or as an “example”in not necessarily to be construed as preferred or advantageous overother aspects or embodiments.

FIG. 1 illustrates an exemplary crowdsourcing system according to someaspects of the present disclosure. As shown in FIG. 1, a crowdsourcingserver 102 may conduct crowdsourcing from one or more vehicles 104, suchas vehicle 1 (104 a), vehicle 2 (104 b), vehicle 3 (104 c), and vehiclen (104 d), for example. A vehicle may also be referred to as a mobileclient or mobile station.

FIG. 2 illustrates an exemplary block diagram of a crowdsourcing clientaccording to some aspects of the present disclosure. In someimplementations, an on board diagnostics (OBD) module 202 may collectvehicle information such as odometer readings, speedometer readings,collision/impact data, fuel consumption, steering data, etc. The OBDmodule 202 may then send the data collected to a navigation controller204. The navigation controller 204 may be configured to integrate theOBD information with a map 206, and GNSS (or GPS/SPS) readings from GNSSmodule 208 to form crowdsourcing data. The navigation controller 204 maythen transmit the crowdsourcing data to a database associated with thecrowdsourcing server 102. The crowdsourcing server 102 may be configuredto data-mine the crowdsourcing data, constructing tables of roadsegments and associated fuel consumption, acceleration and deceleration,average speed, segment entry and exit, and unusual events such as suddenbraking, swerving and impacts, in order to gather trip data and routeconditions for certain road segments. For example, the crowdsourcingdata can be analyzed to determine if a road work has been developed, orif an accident has occurred on a road segment. In addition, based on thecrowdsourcing data, traffic light patterns for various routes can alsobe extracted, from patterns of starts, stops, and accelerations ofvehicle on the road.

In some other implementations, a personal navigation device (PND) may beconfigured to read OBD2 data from vehicle 104 (for example overBluetooth) and packages the OBD2 data with onboard GPS location and timedata. The PND may then upload the packaged information to thecrowdsourcing server 102. When selecting a route, the PND may query thecrowdsourcing server 102 for similar journey and optionally similarvehicles in order to present the user with informed route choices.According to aspects of the present disclosure, pothole data or obstaclemay be extracted. For example, if steering data from OBD2 indicates thatmultiple vehicles exhibit sharp swerving at the same point on a road.Route choices may be extracted from server data, by interpreting trafficlight patterns, actual recorded speed on roads, fuel consumptionstatistics, percentage of braking and acceleration applied, etc. Inaddition, collision data may also be extracted to produce a riskheat-map for various roads and routes.

According to aspects of the present disclosure, parking patterns can beextracted. For example, when a vehicle enters a city and repeatedlyfollows certain loops near the intended destination. This behavior mayindicate searching for a parking space, with the vehicle's location onstopping being recorded. Fuel status before starting can be comparedwith previous crowdsourced fuel consumption patterns for the determinedroutes in order to a) suggest better fuel consumption routes, or b)suggest a route via a fueling station for refuel. According aspects ofthe present disclosure, the term fuel includes but not limited togasoline, diesel, biofuel, and electric charging.

According to aspects of the present disclosure, traffic light patternsand congestion patterns can be extracted from crowdsourcing data todetermine routes through cities based on time of journey to avoidcongestion or red light waves. Crowdsourcing data can support dynamicupdate of routes to determine speed restrictions introduced due to roadworks, damage to road surface, etc. for making better route decisions.Use of refueling sites can also be extracted along with location fromthe crowdsourcing data. This may indicate the popularity of variousfueling stations, which could be linked to a) low prices, and b) qualityof service. Types of fuel used may also be included in OBD2 data,allowing crowdsourced information on a) which fuel types are actuallysold at given fueling stations, or b) if a particular fuel has run out(possibly indicated by car entering and exiting station withoutrefueling). As vehicle identification number (VIN) can be extracted fromOBD2 data, the disclosed method also enables vehicle recovery insituations when a vehicle may be stolen or its location may beforgotten.

According to aspects of the present disclosure, parking availability canbe extracted by comparing the time of arriving at a destination, andsubsequent distance and locations visited before the vehicle is detectedas coming to a halt, for example the engine is turned off. Thisinformation allows the crowdsourcing database to mine for information onhow long it may take to park after the destination is reached, and howfar the vehicle has to travel to reach a suitable parking space.

According to aspects of the present disclosure, in some approaches, thefollowing categories of data may be collected from one or more vehicles104, including but not limited to: vehicle model, manufacture year, fuelsystem status, engine load value, engine revolutions per minute (RPM),speed, intake air temperature, run time since engine start, fuel level,barometric pressure, accelerator pedal position, cruise control, brakepressed, park/neutral position, motion sensor readings, odometerreadings, and steering angle readings. In addition, the followingcategories of data may be collected from a PND, including but notlimited to: latitude, longitude, altitude, velocity, time, and headingof the vehicle. According to some aspects of the present disclosure, thedata collected may be applied to provide navigation guidance asdescribed in association with FIG. 3A and FIG. 3B. The data collectedmay also be applied to warn users of potential road work, dangeroussituations, etc. as described in association with FIG. 7 and FIG. 8.

FIG. 3A and FIG. 3B illustrate an exemplary method of providingnavigation guidance using crowdsourcing data according to some aspectsof the present disclosure. In the example shown in FIG. 3A, a section ofa map may be split into road segments. A line connecting two circlesindicates a road segment, and a circle indicates an intersection betweentwo or more road segments. There may be numerous options to travel froma start location 302 to an end location 304. For example, one path mayinclude road segments A1, A2, and A3; another path may include roadsegments A1, A2, D1, and B3; yet another path may include road segmentsB1, B2, and B3; yet another path may include road segments B1, D2, andC3; yet another path may include road segments C1, C2 and C3.

Conventional navigation systems may determine one of the paths to travelbased on distance, posted speed limits, and in some instances based ontraffic report provided by government agencies or by private entities.One of the problems with the conventional real-time traffic system isthat some of the traffic conditions may not be reported. For example, atraffic condition at certain intersections when a school is dismissedwould not be included in the traffic report; for another example, a roadwork or lane close may not be reported by the conventional real-timetraffic report. All such conditions may alter the time it may take totravel certain segment of a road from the start location 302 to the endlocation 304.

According to some aspects of the present disclosure, the real-timecrowdsourcing data may be crowdsourced dynamically and may be collectedover a period of time. Information such as average fuel consumption,minimum, maximum, and average time to travel certain segment of a roadmay be extracted from GNSS/OBD2 crowdsourcing data. The information maybe updated regularly as more data may be reported by vehicles using thesystem. The analyzed information may then be reported back to PNDs withthe most fuel efficient route, the fastest route, or the route involvingleast stop/starts for example, depending on what information may beimportant to a user. Note that the data collected and the navigationguidance may reflect real recorded conditions, not estimated conditions.Such conditions may be automatically updated without manual interventionand/or installation of expensive electronic monitoring equipment asconditions change.

According to some aspects of the present disclosure, the disclosedmethod may be able to determine a more accurate estimated trip time fora particular road segment based on the particular date, and theparticular time of the day as shown in association with the descriptionin FIG. 3B. In FIG. 3B, each period may describe a certain time periodfor certain date. For example, for road segment A1, period 1 may includethe most up-to-date information crowdsourced over the last 10 minutes,which indicates an average travel time for segment A1 may be 500seconds; period 2 may describe the average travel time for Mondaymorning between 8:00 AM to 8:30 AM, which may be 525 seconds; period Nmay describe the average travel time for Sunday from 9:00 PM to 12:00AM, which may require time T₁, and so on.

In some situations, an average travel time at 3 PM on Sunday for asegment of a road near a school may be different than the average traveltime at 3 PM on weekdays, etc. In some other situations, even though aschool may be dismissed at 3 PM on most weekdays, there may be aparticular weekday there is no school. The crowdsourcing server 102 maybe configured to analyze the crowdsourcing data and determine that thereis no traffic at certain intersection near the school even though theserver may not have the information the school being closed on that day.As a result, the crowdsourcing server 102 may still be able to providenavigation guidance using the actual situation associated with thatsegment of the road. In one implementation, the crowdsourcing server 102may be configured to use more weight on the crowdsourcing data beingrecently collected, for example being collected in the last 5, 10 or 30minutes; and use less weight on the crowdsourcing data that have beencollected over a longer period of time, such as collected one day, oneweek, or one month ago. In the event when no recently collected dataavailable, the crowdsourcing server 102 may use historical data aboutcertain segment of a road established from previous crowdsourcingactivities.

According to aspects of the present disclosure, the crowdsourcing datacollected may be applied to detect traffic light patterns and congestionpatterns for certain road segments and certain intersections. Forexample, the crowdsourcing server may be configured to determine,including but not limited to: 1) streets around a sports venue incertain city may be congested around 10 PM on weekend nights becausethere may be sports events or concerts that typically end around 10 PMon weekend evenings; 2) traffic may be heavier shortly after a museum orshopping mall is closed. Such data may be mined through thecrowdsourcing described above and may be applied to provide moreinformation to users.

FIG. 4 illustrates an exemplary block diagram of a crowdsourcing serveraccording to some aspects of the present disclosure. According toaspects of the present disclosure, the functions described in FIG. 1,FIG. 3A, FIG. 3B, and FIGS. 6-8 may be implemented by the crowdsourcingserver of FIG. 4. In some implementations, the functions may beperformed by processor(s), software, hardware, and firmware, or acombination of these blocks to perform various functions of thecrowdsourcing server described herein, including the functions performedby the crowdsourcing data controller 132 and the navigation guidancemodule 134.

As shown in the exemplary block diagram of FIG. 4, the crowdsourcingserver 102 includes a control unit 120. The control unit 120 may includeone or more processors 122 and associated memory/storage 124. Thecontrol unit 120 may also include software 126, as well as hardware 128,and firmware 130. The control unit 120 includes a crowdsourcing datacontroller 132 configured to collect crowdsourcing data from one or morevehicles 104 and from third party uploads. The control unit 120 furtherincludes a navigation guidance module 134 configured to providenavigation guidance to one or more vehicles 104. The crowdsourcing datacontroller 132 and navigation guidance module 134 are illustratedseparately from processor 122 and/or hardware 128 for clarity, but maybe combined and/or implemented in the processor 122 and/or hardware 128based on instructions in the software 126 and the firmware 130.

The crowdsourcing server 102 may further include a network interface 140and a database interface 142 communicatively coupled to the control unit120. The network interface can be configured to enable the crowdsourcingserver 102 to communicate with other servers, computers, mobile devices,and vehicles via one or more communication networks. The databaseinterface can be configured to enable the crowdsourcing server 102 tocommunicate with one or more crowdsourcing databases.

Note that control unit 120 can be configured to implement methods ofproviding navigation guidance to vehicles. For example, the control unit120 can be configured to implement functions of the crowdsourcing server102 described in FIG. 1, FIG. 3A-3B and FIG. 5-8.

FIG. 5 illustrates an exemplary block diagram of a crowdsourcing datacontroller of FIG. 4 according to some aspects of the presentdisclosure. In the example shown in FIG. 5, crowdsourcing datacontroller 132 of crowdsourcing server 102 includes crowdsourcing dataaggregation block 506, crowdsourcing data 508, third party data merger510, third party data 512, and record integrator 514. The crowdsourcingdata aggregation block 506 receives information from vehicle uploads 502and stores the data in the crowdsourcing data 508. Similarly, the thirdparty data merger 510 receives information from third party uploads 504and stores the data in the third party data 512. The record integrator514 receives data from both the crowdsourcing data aggregation block 506and the third party data merger 510 and stores the data in theintegrated crowdsourcing database 516.

According to aspects of the present disclosure, the data accumulationprocess may be based on time limit and number of measurements. Varioustime limits on data collection may be implemented. In one exemplaryimplementation, the data accumulation process checks incoming vehicleupload data with periodicity of a predetermined period (for example 5minutes, 30 minutes, 1 hour, 6 hours, 1 day, etc.). Next, the processquantizes latitude and longitude of reported vehicle position toapproximate 0.00002 degree (2 m). The quantization resolution (i.e. thesize of grids) can be configurable and can be adjusted depending onupload data density. For each vehicle/mobile fix, the data accumulationprocess checks if the quantized grid is occupied. If occupied, theprocess puts the fix into this grid's measurement record, and increasesthis grid's number of measurement. Alternatively, the process adds thismobile fix to this grid's record (grid's number of measurement isincremented by 1) and increases the number of data points in theaccumulated unique grids (numUniqueGrids). Then, the process adds thecrowdsourcing data to the aggregation list if sufficient data isaccumulated (for example numUniqueGrids>=3). Next, the process adds thecrowdsourcing data to the aggregation list if there is no sufficientdata, but a predetermined maximum accumulation time is reached (forexample 14 days). Last but not least, if the aggregation list is notempty, the process makes an aggregation request to update the integratedcrowdsourcing database 516.

According to aspects of the present disclosure, the third party datamerger 510 can be configured to update the third party data 512 whenthere are multiple injections of third party uploads, whether such dataare received from the same provider or different providers. In the eventof the multiple third party data injections are from the same provider,then the old data may be replaced by the new data. If the multiple thirdparty data injections are from different providers, the third party datamerger can be configured to choose a database as the primary database,and the other databases may be compared to the primary database. If anew record is found in other databases, that record can be added to theprimary database. In some implementations, when the multiple third partydatabases have a unified reliability level metric on each of theirrecords, the record that has the highest reliability level may beselected by the third party data merger 510 and put into the third partydata 512.

According to aspects of the present disclosure, the crowdsourcing dataaggregation block 506 and the third party data merger 510 can be twoparallel operations. At the end of each operation process, the resultcan be saved in their own database respectively. The results from bothoperations can then be combined by the record integrator 514.

In some implementations, the record integrator 514 may be configured toperform the following tasks. First, the record integrator 514 can beconfigured to estimate the integrated result using crowdsourcing datamade available to the record integrator 514. Records based on thirdparty uploads may be made available to the record integrator 514 afterthe data has been provided by a third party. Note that initially, thirdparty data records may be cached in the record integrator 514 and thenstored in the integrated crowdsourcing database 516. If results fromboth third party data 512 and the crowdsourcing data 508 are available,the record integrator 514 may be configured to choose results from thecrowdsourcing data 508 through the crowdsourcing data aggregation block506.

FIG. 6 illustrates a method of providing navigation guidance accordingto some aspects of the present disclosure. As shown in FIG. 6, in block602, the method receives crowdsourcing data from a plurality ofvehicles. In block 604, the method determines information correspondingto a route of interest to at least one vehicle using the crowdsourcingdata. In block 606, the method provides navigation guidance to the atleast one vehicle using the information determined. The crowdsourcingdata includes on board diagnostics data (OBD) correlated with timestamps and GPS locations of a vehicle, and where the on boarddiagnostics data includes odometer information, speedometer information,fuel consumption information, steering information, and impact data.

According to some aspects of the present disclosure, the methodsperformed in block 604 may further include methods performed in blocks608 to 618. In block 608, the method predicts closing of a lane usingthe crowdsourcing data, predicts an avoidance maneuver using thecrowdsourcing data, predicts a congestion with respect to a segment ofthe route of the at least one vehicle using the crowdsourcing data,and/or predicts traffic light patterns using the crowdsourcing data. Inaddition, the method monitors changes of vehicle direction at a locationon the route of the at least one vehicle, and determines a lane isclosed in response to a number of changes of vehicle direction beinglarger than a predetermined threshold value. Moreover, the methodmonitors change of vehicle direction and distance traveled at a closevicinity of a location on the route of the at least one vehicle, anddetermines an avoidance maneuver in response to a ratio of change ofvehicle direction and distance traveled being less than a predeterminedthreshold value.

In block 610, determines a route based at least in part on an amount oftime predicted for travelling from a starting location to a destinationlocation of the route using the crowdsourcing data and/or determines aroute based at least in part on a predicted fuel consumption of theroute using the crowdsourcing data.

In block 612, the method monitors a distance traveled by the at leastone vehicle after reaching a destination, and predicts availability ofparking spaces at the destination based at least in part on the distancetraveled; and/or monitors an amount of time traveled by the at least onevehicle after reaching a destination, and predicts availability ofparking spaces at the destination based at least in part on the amountof time traveled.

In block 614, the method measures a time taken to travel a predefinedpercent of the route until the at least one vehicle comes to a halt at apredetermined location; and/or predicts an average amount of time usedto find parking at the predetermined location using the time taken totravel a predefined percent of the route.

In block 616, the method determines popularity of a fueling stationalong the route, determines type of fuel sold at the fueling stationalong the route, determines popularity of a business along the route,and/or determines popularity of a rest area along the route. In block618, the method determines location of a stolen vehicle in accordancewith crowdsourcing data received from the stolen vehicle.

FIG. 7 illustrates a method of detecting an avoidance maneuver accordingto some aspects of the present disclosure. As shown in FIG. 7, at timet1, a vehicle 702 (labeled as 702 a) may have an angle of heading h1. Inthis exemplary implementation, the angle of heading may be describedrelative to a reference coordinate 704. A plot 708 can be used to showthe angle of heading of the vehicle 702 versus time. At time t1, h1 maybe approximately equal to 90°. At time t2, to avoid an obstacle 706, thevehicle 702 (labeled as 702 b) may swerve sharply to the right and mayhave a heading of h2, which may be approximately equal to 60° as shownin the plot 708. At time t3, the vehicle 702 (labeled as 702 c) hasmoved to the lane on the right with a heading h3, which may beapproximately equal to h1 of 90°. At t4, the vehicle 702 (labeled as 702d) continues with heading h3.

According to some aspects of the present disclosure, information aboutan avoidance maneuver as described in association with FIG. 7 above maybe gathered by the vehicle 702 (labeled as 702 a-702 d) and be reportedto a crowdsourcing server. In addition, as the crowdsourcing datashowing more vehicles that perform a similar avoidance maneuver byperforming a sharp swerve, the crowdsourcing server may analyze suchdata and be able to predict such unusual/dangerous condition with ahigher confidence. The crowdsourcing server may broadcast this dangerouscondition to other vehicles that may travel to this section of the road.

FIG. 8 illustrates a method of detecting closing of a lane according tosome aspects of the present disclosure. As shown in FIG. 8, at time t1,a vehicle 802 (labeled as 802 a) may have an angle of heading h1. Inthis exemplary implementation, the angle of heading may be describedrelative to a reference coordinate 804. A plot 808 can be used to showthe angle of heading of the vehicle 802 versus time. At time t1, h1 maybe approximately equal to 90°. The vehicle 802 (labeled as 802 a) mayhave observed a series of warning signs, such as the safety cones 806 ato 806 g. At time t2, to avoid the series of safety cones 806 a to 806g, the vehicle 802 (labeled as 802 b) may change lane to the right andmay have a heading of h2, which may be approximately equal to 75° asshown in the plot 808. At time t3, the vehicle 802 (labeled as 802 c)may have a heading h3, which may be approximately equal to 85°. At t4,the vehicle 802 (labeled as 802 d) has moved to the lane on the rightwith heading h4, may be approximately equal to h1 of 90°.

According to some aspects of the present disclosure, information aboutthe behavior of a vehicle that travels on a section of a road asdescribed in association with FIG. 8 above may be gathered and may bereported to a crowdsourcing server. In addition, as the crowdsourcingdata showing more vehicles that demonstrate a similar behavior byperforming a lane change at approximately the same location, thecrowdsourcing server may be able to analyze the data and predict suchsituation of a lane closure with a higher confidence. The crowdsourcingserver may broadcast such road condition to other vehicles that maytravel to this section of the road.

According to some aspects of the present disclosure, the crowdsourcingserver may be configured to analyze the location and the change in angleof heading as indicated by the plot 708 to determine an avoidancemaneuver. Similarly, the crowdsourcing server may be configured toanalyze the location and the change in angle of heading as indicated bythe plot 808 to determine a condition of lane closure. Furthermore, thecrowdsourcing server may be configured to analyze the difference betweenplots 708 and plot 808 to distinguish the situation described in FIG. 7from the situation described in FIG. 8.

According to some aspects of the present disclosure, the change in angleof heading of a vehicle may be measured relative to distance traveled bythe vehicle as opposed to relative to time as shown in plot 708 and 808.The crowdsourcing server may be configured to analyze the crowdsourcingdata and may be able to make similar determinations as described in FIG.7 and FIG. 8 based on the data of changing in angle of heading of avehicle relative to distance traveled by the vehicle.

Note that at least paragraphs [0061]-[0063], FIG. 1-2, FIG. 4-6 andtheir corresponding descriptions provide means for receivingcrowdsourcing data from a plurality of vehicles, means for determininginformation corresponding to a route of interest to at least one vehicleusing the crowdsourcing data, and means for providing navigationguidance to the at least one vehicle using the information determined.

The methodologies and mobile device described herein can be implementedby various means depending upon the application. For example, thesemethodologies can be implemented in hardware, firmware, software, or acombination thereof. For a hardware implementation, the processing unitscan be implemented within one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, electronic devices, other electronicunits designed to perform the functions described herein, or acombination thereof. Herein, the term “control logic” encompasses logicimplemented by software, hardware, firmware, or a combination.

For a firmware and/or software implementation, the methodologies can beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine readable mediumtangibly embodying instructions can be used in implementing themethodologies described herein. For example, software codes can bestored in a memory and executed by a processing unit. Memory can beimplemented within the processing unit or external to the processingunit. As used herein the term “memory” refers to any type of long term,short term, volatile, nonvolatile, or other storage devices and is notto be limited to any particular type of memory or number of memories, ortype of media upon which memory is stored.

If implemented in firmware and/or software, the functions may be storedas one or more instructions or code on a computer-readable medium.Examples include computer-readable media encoded with a data structureand computer-readable media encoded with a computer program.Computer-readable media may take the form of an article of manufacturer.Computer-readable media includes physical computer storage media and/orother non-transitory media. A storage medium may be any available mediumthat can be accessed by a computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to storedesired program code in the form of instructions or data structures andthat can be accessed by a computer; disk and disc, as used herein,includes compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and Blu-ray disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Combinations of the above should also be included within the scope ofcomputer-readable media.

In addition to storage on computer readable medium, instructions and/ordata may be provided as signals on transmission media included in acommunication apparatus. For example, a communication apparatus mayinclude a transceiver having signals indicative of instructions anddata. The instructions and data are configured to cause one or moreprocessors to implement the functions outlined in the claims. That is,the communication apparatus includes transmission media with signalsindicative of information to perform disclosed functions. At a firsttime, the transmission media included in the communication apparatus mayinclude a first portion of the information to perform the disclosedfunctions, while at a second time the transmission media included in thecommunication apparatus may include a second portion of the informationto perform the disclosed functions.

The disclosure may be implemented in conjunction with various wirelesscommunication networks such as a wireless wide area network (WWAN), awireless local area network (WLAN), a wireless personal area network(WPAN), and so on. The terms “network” and “system” are often usedinterchangeably. The terms “position” and “location” are often usedinterchangeably. A WWAN may be a Code Division Multiple Access (CDMA)network, a Time Division Multiple Access (TDMA) network, a FrequencyDivision Multiple Access (FDMA) network, an Orthogonal FrequencyDivision Multiple Access (OFDMA) network, a Single-Carrier FrequencyDivision Multiple Access (SC-FDMA) network, a Long Term Evolution (LTE)network, a WiMAX (IEEE 802.16) network and so on. A CDMA network mayimplement one or more radio access technologies (RATs) such as cdma2000,Wideband-CDMA (W-CDMA), and so on. Cdma2000 includes IS-95, IS2000, andIS-856 standards. A TDMA network may implement Global System for MobileCommunications (GSM), Digital Advanced Mobile Phone System (D-AMPS), orsome other RAT. GSM and W-CDMA are described in documents from aconsortium named “3rd Generation Partnership Project” (3GPP). Cdma2000is described in documents from a consortium named “3rd GenerationPartnership Project 2” (3GPP2). 3GPP and 3GPP2 documents are publiclyavailable. A WLAN may be an IEEE 802.11x network, and a WPAN may be aBluetooth network, an IEEE 802.15x, or some other type of network. Thetechniques may also be implemented in conjunction with any combinationof WWAN, WLAN and/or WPAN.

A mobile station refers to a device such as a cellular or other wirelesscommunication device, personal communication system (PCS) device,personal navigation device (PND), Personal Information Manager (PIM),Personal Digital Assistant (PDA), laptop or other suitable mobile devicewhich is capable of receiving wireless communication and/or navigationsignals. The term “mobile station” is also intended to include deviceswhich communicate with a personal navigation device (PND), such as byshort-range wireless, infrared, wire line connection, or otherconnection—regardless of whether satellite signal reception, assistancedata reception, and/or position-related processing occurs at the deviceor at the PND. Also, “mobile station” is intended to include alldevices, including wireless communication devices, computers, laptops,etc. which are capable of communication with a server, such as via theInternet, Wi-Fi, or other network, and regardless of whether satellitesignal reception, assistance data reception, and/or position-relatedprocessing occurs at the device, at a server, or at another deviceassociated with the network. Any operable combination of the above arealso considered a “mobile station.”

Designation that something is “optimized,” “required” or otherdesignation does not indicate that the current disclosure applies onlyto systems that are optimized, or systems in which the “required”elements are present (or other limitation due to other designations).These designations refer only to the particular describedimplementation. Of course, many implementations are possible. Thetechniques can be used with protocols other than those discussed herein,including protocols that are in development or to be developed.

One skilled in the relevant art will recognize that many possiblemodifications and combinations of the disclosed embodiments may be used,while still employing the same basic underlying mechanisms andmethodologies. The foregoing description, for purposes of explanation,has been written with references to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the disclosure to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described to explain the principles of thedisclosure and their practical applications, and to enable othersskilled in the art to best utilize the disclosure and variousembodiments with various modifications as suited to the particular usecontemplated.

We claim:
 1. A method of providing navigation guidance to vehicles,comprising: receiving crowdsourcing data from a plurality of vehicles;determining information corresponding to a route of interest to at leastone vehicle using the crowdsourcing data; and providing navigationguidance to the at least one vehicle using the information determined.2. The method of claim 1, wherein the crowdsourcing data includes onboard diagnostics data (OBD) correlated with time stamps and GPSlocations of a vehicle; and wherein the on board diagnostics dataincludes odometer information, speedometer information, fuel consumptioninformation, steering information, and impact data.
 3. The method ofclaim 1, wherein determining information corresponding to a route ofinterest to at least one vehicle comprises at least one of: predictingclosing of a lane using the crowdsourcing data; predicting an avoidancemaneuver using the crowdsourcing data; predicting a congestion withrespect to a segment of the route of the at least one vehicle using thecrowdsourcing data; and predicting traffic light patterns using thecrowdsourcing data.
 4. The method of claim 3, wherein predicting closingof a lane comprises: monitoring changes of vehicle direction at alocation on the route of the at least one vehicle; and determining alane is closed in response to a number of changes of vehicle directionbeing larger than a predetermined threshold value.
 5. The method ofclaim 3, wherein predicting an avoidance maneuver comprises: monitoringchange of vehicle direction and distance traveled at a close vicinity ofa location on the route of the at least one vehicle; and determining anavoidance maneuver in response to a ratio of change of vehicle directionand distance traveled being less than a predetermined threshold value.6. The method of claim 1, wherein determining information correspondingto a route of interest to at least one vehicle further comprises atleast one of: determining a route based at least in part on an amount oftime predicted for travelling from a starting location to a destinationlocation of the route using the crowdsourcing data; and determining aroute based at least in part on a predicted fuel consumption of theroute using the crowdsourcing data.
 7. The method of claim 1, whereindetermining information corresponding to a route of interest to at leastone vehicle further comprises at least one of: monitoring a distancetraveled by the at least one vehicle after reaching a destination, andpredicting availability of parking spaces at the destination based atleast in part on the distance traveled; and monitoring an amount of timetraveled by the at least one vehicle after reaching a destination, andpredicting availability of parking spaces at the destination based atleast in part on the amount of time traveled.
 8. The method of claim 1,wherein determining information corresponding to a route of interest toat least one vehicle further comprises: measuring a time taken to travela predefined percent of the route until the at least one vehicle comesto a halt at a predetermined location; and predicting an average amountof time used to find parking at the predetermined location using thetime taken to travel a predefined percent of the route.
 9. The method ofclaim 1, wherein determining information corresponding to a route ofinterest to at least one vehicle further comprises at least one of:determining popularity of a fueling station along the route; determiningtype of fuel sold at the fueling station along the route; determiningpopularity of a business along the route; and determining popularity ofa rest area along the route.
 10. The method of claim 1, whereindetermining information corresponding to a route of interest to at leastone vehicle further comprises: determining location of a stolen vehiclein accordance with crowdsourcing data received from the stolen vehicle.11. A computer program product comprising non-transitory medium storinginstructions for execution by one or more computer systems, theinstructions comprising: instructions for receiving crowdsourcing datafrom a plurality of vehicles; instructions for determining informationcorresponding to a route of interest to at least one vehicle using thecrowdsourcing data; and instructions for providing navigation guidanceto the at least one vehicle using the information determined.
 12. Thecomputer program product of claim 11, wherein the crowdsourcing dataincludes on board diagnostics data (OBD) correlated with time stamps andGPS locations of a vehicle; and wherein the on board diagnostics dataincludes odometer information, speedometer information, fuel consumptioninformation, steering information, and impact data.
 13. The computerprogram product of claim 11, wherein instructions for determininginformation corresponding to a route of interest to at least one vehiclecomprises at least one of: instructions for predicting closing of a laneusing the crowdsourcing data; instructions for predicting an avoidancemaneuver using the crowdsourcing data; instructions for predicting acongestion with respect to a segment of the route of the at least onevehicle using the crowdsourcing data; and instructions for predictingtraffic light patterns using the crowdsourcing data.
 14. The computerprogram product of claim 13, wherein instructions for predicting closingof a lane comprises: instructions for monitoring changes of vehicledirection at a location on the route of the at least one vehicle; andinstructions for determining a lane is closed in response to a number ofchanges of vehicle direction being larger than a predetermined thresholdvalue.
 15. The computer program product of claim 13, whereininstructions for predicting an avoidance maneuver comprises:instructions for monitoring change of vehicle direction and distancetraveled at a close vicinity of a location on the route of the at leastone vehicle; and instructions for determining an avoidance maneuver inresponse to a ratio of change of vehicle direction and distance traveledbeing less than a predetermined threshold value.
 16. The computerprogram product of claim 11, wherein instructions for determininginformation corresponding to a route of interest to at least one vehiclefurther comprises at least one of: instructions for determining a routebased at least in part on an amount of time predicted for travellingfrom a starting location to a destination location of the route usingthe crowdsourcing data; and instructions for determining a route basedat least in part on a predicted fuel consumption of the route using thecrowdsourcing data.
 17. The computer program product of claim 11,wherein instructions for determining information corresponding to aroute of interest to at least one vehicle further comprises at least oneof: instructions for monitoring a distance traveled by the at least onevehicle after reaching a destination, and predicting availability ofparking spaces at the destination based at least in part on the distancetraveled; and instructions for monitoring an amount of time traveled bythe at least one vehicle after reaching a destination, and predictingavailability of parking spaces at the destination based at least in parton the amount of time traveled.
 18. The computer program product ofclaim 11, wherein instructions for determining information correspondingto a route of interest to at least one vehicle further comprises:instructions for measuring a time taken to travel a predefined percentof the route until the at least one vehicle comes to a halt at apredetermined location; and instructions for predicting an averageamount of time used to find parking at the predetermined location usingthe time taken to travel a predefined percent of the route.
 19. Thecomputer program product of claim 11, wherein instructions fordetermining information corresponding to a route of interest to at leastone vehicle further comprises at least one of: instructions fordetermining popularity of a fueling station along the route;instructions for determining type of fuel sold at the fueling stationalong the route; instructions for determining popularity of a businessalong the route; and instructions for determining popularity of a restarea along the route.
 20. The computer program product of claim 11,wherein instructions for determining information corresponding to aroute of interest to at least one vehicle further comprises:instructions for determining location of a stolen vehicle in accordancewith crowdsourcing data received from the stolen vehicle.
 21. Anapparatus, comprising: a control unit including processing logic, theprocessing logic comprising: logic configured to receive crowdsourcingdata from a plurality of vehicles; logic configured to determineinformation corresponding to a route of interest to at least one vehicleusing the crowdsourcing data; and logic configured to provide navigationguidance to the at least one vehicle using the information determined.22. The apparatus of claim 21, wherein the crowdsourcing data includeson board diagnostics data (OBD) correlated with time stamps and GPSlocations of a vehicle; and wherein the on board diagnostics dataincludes odometer information, speedometer information, fuel consumptioninformation, steering information, and impact data.
 23. The apparatus ofclaim 21, wherein logic configured to determine informationcorresponding to a route of interest to at least one vehicle comprisesat least one of: logic configured to predict closing of a lane using thecrowdsourcing data; logic configured to predict an avoidance maneuverusing the crowdsourcing data; logic configured to predict a congestionwith respect to a segment of the route of the at least one vehicle usingthe crowdsourcing data; and logic configured to predict traffic lightpatterns using the crowdsourcing data.
 24. The apparatus of claim 23,wherein logic configured to predict closing of a lane comprises: logicconfigured to monitor changes of vehicle direction at a location on theroute of the at least one vehicle; and logic configured to determine alane is closed in response to a number of changes of vehicle directionbeing larger than a predetermined threshold value.
 25. The apparatus ofclaim 23, wherein logic configured to predict an avoidance maneuvercomprises: logic configured to monitor change of vehicle direction anddistance traveled at a close vicinity of a location on the route of theat least one vehicle; and logic configured to determine an avoidancemaneuver in response to a ratio of change of vehicle direction anddistance traveled being less than a predetermined threshold value. 26.The apparatus of claim 21, wherein logic configured to determineinformation corresponding to a route of interest to at least one vehiclefurther comprises at least one of: logic configured to determine a routebased at least in part on an amount of time predicted for travellingfrom a starting location to a destination location of the route usingthe crowdsourcing data; and logic configured to determine a route basedat least in part on a predicted fuel consumption of the route using thecrowdsourcing data.
 27. The apparatus of claim 21, wherein logicconfigured to determine information corresponding to a route of interestto at least one vehicle further comprises at least one of: logicconfigured to monitor a distance traveled by the at least one vehicleafter reaching a destination, and predicting availability of parkingspaces at the destination based at least in part on the distancetraveled; and logic configured to monitor an amount of time traveled bythe at least one vehicle after reaching a destination, and predictingavailability of parking spaces at the destination based at least in parton the amount of time traveled.
 28. The apparatus of claim 21, whereinlogic configured to determine information corresponding to a route ofinterest to at least one vehicle further comprises: logic configured tomeasure a time taken to travel a predefined percent of the route untilthe at least one vehicle comes to a halt at a predetermined location;and logic configured to predict an average amount of time used to findparking at the predetermined location using the time taken to travel apredefined percent of the route.
 29. The apparatus of claim 21, whereinlogic configured to determine information corresponding to a route ofinterest to at least one vehicle further comprises at least one of:logic configured to determine popularity of a fueling station along theroute; logic configured to determine type of fuel sold at the fuelingstation along the route; logic configured to determine popularity of abusiness along the route; and logic configured to determine popularityof a rest area along the route.
 30. The apparatus of claim 21, whereinlogic configured to determine information corresponding to a route ofinterest to at least one vehicle further comprises: logic configured todetermine location of a stolen vehicle in accordance with crowdsourcingdata received from the stolen vehicle.
 31. A mobile station, comprising:a navigation controller including processing logic, the processing logiccomprising: logic configured to send crowdsourcing data to a server;logic configured to receive navigation guidance from the server; andlogic configured to display the navigation guidance on a display. 32.The mobile station of claim 31, wherein logic configured to sendcrowdsourcing data to the server further comprises: logic configured toreceive on board diagnostics data from an on-board diagnostic module ofa vehicle; logic configured to receive GPS locations of the vehicle froma GNSS module; and logic configured to correlate the on boarddiagnostics data and GPS locations of the vehicle with time stamps togenerate the crowdsourcing data.
 33. The mobile station of claim 32,wherein the on board diagnostics data comprises odometer information,speedometer information, fuel consumption information, steeringinformation, and impact data.
 34. A system, comprising: means forreceiving crowdsourcing data from a plurality of vehicles; means fordetermining information corresponding to a route of interest to at leastone vehicle using the crowdsourcing data; and means for providingnavigation guidance to the at least one vehicle using the informationdetermined.